Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	443	432
Missing cells (%)	8.3%	8.1%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Sex` is highly overall correlated with `Survived`	`Sex` is highly overall correlated with `Survived`	High correlation
`Survived` is highly overall correlated with `Sex`	`Survived` is highly overall correlated with `Sex`	High correlation
`Age` has 91 (20.4%) missing values	`Age` has 89 (20.0%) missing values	Missing
`Cabin` has 350 (78.5%) missing values	`Cabin` has 342 (76.7%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 308 (69.1%) zeros	`SibSp` has 308 (69.1%) zeros	Zeros
`Parch` has 340 (76.2%) zeros	`Parch` has 343 (76.9%) zeros	Zeros
`Fare` has 6 (1.3%) zeros	`Fare` has 11 (2.5%) zeros	Zeros

Reproduction

	Dataset A	Dataset B
Analysis started	2025-03-05 15:19:05.669029	2025-03-05 15:19:08.170735
Analysis finished	2025-03-05 15:19:08.167522	2025-03-05 15:19:10.646825
Duration	2.5 seconds	2.48 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	440.91704	443.95067

	Dataset A	Dataset B
Minimum	5	1
Maximum	890	890
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	5	1
5-th percentile	45.5	46.5
Q1	207.5	205.5
median	432	443.5
Q3	678.75	678.75
95-th percentile	854.75	845.75
Maximum	890	890
Range	885	889
Interquartile range (IQR)	471.25	473.25

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	266.47689	261.86245
Coefficient of variation (CV)	0.60436968	0.58984583
Kurtosis	-1.277178	-1.2697806
Mean	440.91704	443.95067
Median Absolute Deviation (MAD)	236.5	236.5
Skewness	0.06262959	0.0095143986
Sum	196649	198002
Variance	71009.932	68571.944
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
737	1	0.2%
418	1	0.2%
875	1	0.2%
820	1	0.2%
151	1	0.2%
389	1	0.2%
889	1	0.2%
623	1	0.2%
311	1	0.2%
203	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
462	1	0.2%
875	1	0.2%
9	1	0.2%
693	1	0.2%
96	1	0.2%
755	1	0.2%
40	1	0.2%
27	1	0.2%
225	1	0.2%
423	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
5	1	0.2%
6	1	0.2%
7	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
12	1	0.2%
13	1	0.2%
14	1	0.2%
15	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
8	1	0.2%
9	1	0.2%
13	1	0.2%
14	1	0.2%
15	1	0.2%
16	1	0.2%
18	1	0.2%
22	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
8	1	0.2%
9	1	0.2%
13	1	0.2%
14	1	0.2%
15	1	0.2%
16	1	0.2%
18	1	0.2%
22	1	0.2%

Value	Count	Frequency (%)
5	1	0.2%
6	1	0.2%
7	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
12	1	0.2%
13	1	0.2%
14	1	0.2%
15	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	273
1	173

0	278
1	168

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	1	1
2nd row	1	1
3rd row	0	1
4th row	0	0
5th row	0	1

Common Values

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring characters

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	251
1	105
2	90

3	233
1	114
2	99

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	2	2
2nd row	2	3
3rd row	3	3
4th row	2	3
5th row	3	2

Common Values

Value	Count	Frequency (%)
3	251	56.3%
1	105	23.5%
2	90	20.2%

Value	Count	Frequency (%)
3	233	52.2%
1	114	25.6%
2	99	22.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	251	56.3%
1	105	23.5%
2	90	20.2%

Value	Count	Frequency (%)
3	233	52.2%
1	114	25.6%
2	99	22.2%

Most occurring characters

Value	Count	Frequency (%)
3	251	56.3%
1	105	23.5%
2	90	20.2%

Value	Count	Frequency (%)
3	233	52.2%
1	114	25.6%
2	99	22.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	251	56.3%
1	105	23.5%
2	90	20.2%

Value	Count	Frequency (%)
3	233	52.2%
1	114	25.6%
2	99	22.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	251	56.3%
1	105	23.5%
2	90	20.2%

Value	Count	Frequency (%)
3	233	52.2%
1	114	25.6%
2	99	22.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	251	56.3%
1	105	23.5%
2	90	20.2%

Value	Count	Frequency (%)
3	233	52.2%
1	114	25.6%
2	99	22.2%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	65	82
Median length	48	47
Mean length	26.008969	27.116592
Min length	12	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	11600	12094
Distinct characters	60	59
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Silven, Miss. Lyyli Karoliina	Abelson, Mrs. Samuel (Hannah Wizosky)
2nd row	Abelson, Mrs. Samuel (Hannah Wizosky)	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)
3rd row	Skoog, Master. Karl Thorsten	Lam, Mr. Ali
4th row	Bateman, Rev. Robert James	Shorney, Mr. Charles Joseph
5th row	Sadlier, Mr. Matthew	Herman, Mrs. Samuel (Jane Laver)

Value	Count	Frequency (%)
mr	258	14.6%
miss	102	5.8%
mrs	61	3.5%
william	29	1.6%
henry	18	1.0%
john	18	1.0%
master	17	1.0%
george	14	0.8%
mary	12	0.7%
james	12	0.7%
Other values (874)	1224	69.3%

Value	Count	Frequency (%)
mr	266	14.6%
miss	90	5.0%
mrs	63	3.5%
william	34	1.9%
john	24	1.3%
henry	16	0.9%
master	16	0.9%
charles	15	0.8%
thomas	14	0.8%
george	14	0.8%
Other values (902)	1265	69.6%

Most occurring characters

Value	Count	Frequency (%)
	1319	11.4%
r	941	8.1%
e	828	7.1%
a	798	6.9%
i	655	5.6%
s	636	5.5%
n	635	5.5%
M	561	4.8%
l	502	4.3%
o	467	4.0%
Other values (50)	4258	36.7%

Value	Count	Frequency (%)
	1373	11.4%
r	1005	8.3%
e	866	7.2%
a	832	6.9%
i	672	5.6%
n	656	5.4%
s	641	5.3%
M	553	4.6%
l	541	4.5%
o	523	4.3%
Other values (49)	4432	36.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	11600	100.0%

Value	Count	Frequency (%)
(unknown)	12094	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1319	11.4%
r	941	8.1%
e	828	7.1%
a	798	6.9%
i	655	5.6%
s	636	5.5%
n	635	5.5%
M	561	4.8%
l	502	4.3%
o	467	4.0%
Other values (50)	4258	36.7%

Value	Count	Frequency (%)
	1373	11.4%
r	1005	8.3%
e	866	7.2%
a	832	6.9%
i	672	5.6%
n	656	5.4%
s	641	5.3%
M	553	4.6%
l	541	4.5%
o	523	4.3%
Other values (49)	4432	36.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	11600	100.0%

Value	Count	Frequency (%)
(unknown)	12094	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1319	11.4%
r	941	8.1%
e	828	7.1%
a	798	6.9%
i	655	5.6%
s	636	5.5%
n	635	5.5%
M	561	4.8%
l	502	4.3%
o	467	4.0%
Other values (50)	4258	36.7%

Value	Count	Frequency (%)
	1373	11.4%
r	1005	8.3%
e	866	7.2%
a	832	6.9%
i	672	5.6%
n	656	5.4%
s	641	5.3%
M	553	4.6%
l	541	4.5%
o	523	4.3%
Other values (49)	4432	36.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	11600	100.0%

Value	Count	Frequency (%)
(unknown)	12094	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1319	11.4%
r	941	8.1%
e	828	7.1%
a	798	6.9%
i	655	5.6%
s	636	5.5%
n	635	5.5%
M	561	4.8%
l	502	4.3%
o	467	4.0%
Other values (50)	4258	36.7%

Value	Count	Frequency (%)
	1373	11.4%
r	1005	8.3%
e	866	7.2%
a	832	6.9%
i	672	5.6%
n	656	5.4%
s	641	5.3%
M	553	4.6%
l	541	4.5%
o	523	4.3%
Other values (49)	4432	36.6%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	281
female	165

male	289
female	157

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.7399103	4.7040359
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2114	2098
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	female	female
2nd row	female	female
3rd row	male	male
4th row	male	male
5th row	male	female

Common Values

Value	Count	Frequency (%)
male	281	63.0%
female	165	37.0%

Value	Count	Frequency (%)
male	289	64.8%
female	157	35.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	281	63.0%
female	165	37.0%

Value	Count	Frequency (%)
male	289	64.8%
female	157	35.2%

Most occurring characters

Value	Count	Frequency (%)
e	611	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	165	7.8%

Value	Count	Frequency (%)
e	603	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	157	7.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2114	100.0%

Value	Count	Frequency (%)
(unknown)	2098	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	611	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	165	7.8%

Value	Count	Frequency (%)
e	603	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	157	7.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2114	100.0%

Value	Count	Frequency (%)
(unknown)	2098	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	611	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	165	7.8%

Value	Count	Frequency (%)
e	603	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	157	7.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2114	100.0%

Value	Count	Frequency (%)
(unknown)	2098	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	611	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	165	7.8%

Value	Count	Frequency (%)
e	603	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	157	7.5%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	78	75
Distinct (%)	22.0%	21.0%
Missing	91	89
Missing (%)	20.4%	20.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	29.961746	30.252577

	Dataset A	Dataset B
Minimum	0.42	0.67
Maximum	80	71
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.42	0.67
5-th percentile	5	6.8
Q1	20	21
median	28	28.5
Q3	39	39
95-th percentile	58	56.2
Maximum	80	71
Range	79.58	70.33
Interquartile range (IQR)	19	18

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	14.983298	14.189995
Coefficient of variation (CV)	0.50008092	0.46905079
Kurtosis	0.27959368	0.10180537
Mean	29.961746	30.252577
Median Absolute Deviation (MAD)	9	8.5
Skewness	0.51784624	0.43054085
Sum	10636.42	10800.17
Variance	224.49921	201.35597
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
19	17	3.8%
30	16	3.6%
22	15	3.4%
25	13	2.9%
18	13	2.9%
24	13	2.9%
28	12	2.7%
32	11	2.5%
21	11	2.5%
36	10	2.2%
Other values (68)	224	50.2%
(Missing)	91	20.4%

Value	Count	Frequency (%)
24	18	4.0%
30	14	3.1%
28	14	3.1%
18	14	3.1%
22	12	2.7%
29	12	2.7%
25	11	2.5%
19	11	2.5%
34	11	2.5%
26	10	2.2%
Other values (65)	230	51.6%
(Missing)	89	20.0%

Value	Count	Frequency (%)
0.42	1	0.2%
0.67	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
1	4	0.9%
2	2	0.4%
3	1	0.2%
4	5	1.1%
5	4	0.9%
6	2	0.4%

Value	Count	Frequency (%)
0.67	1	0.2%
1	2	0.4%
2	4	0.9%
3	1	0.2%
4	5	1.1%
5	3	0.7%
6	2	0.4%
7	3	0.7%
8	1	0.2%
9	3	0.7%

Value	Count	Frequency (%)
0.67	1	0.2%
1	2	0.4%
2	4	0.9%
3	1	0.2%
4	5	1.1%
5	3	0.7%
6	2	0.4%
7	3	0.7%
8	1	0.2%
9	3	0.7%

Value	Count	Frequency (%)
0.42	1	0.2%
0.67	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
1	4	0.9%
2	2	0.4%
3	1	0.2%
4	5	1.1%
5	4	0.9%
6	2	0.4%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.48430493	0.51793722

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	308	308
Zeros (%)	69.1%	69.1%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	2	3
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.0004383	1.0968356
Coefficient of variation (CV)	2.0657197	2.1176999
Kurtosis	17.610261	16.621726
Mean	0.48430493	0.51793722
Median Absolute Deviation (MAD)	0	0
Skewness	3.5668817	3.5670788
Sum	216	231
Variance	1.0008767	1.2030483
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	308	69.1%
1	104	23.3%
2	12	2.7%
3	11	2.5%
4	6	1.3%
5	3	0.7%
8	2	0.4%

Value	Count	Frequency (%)
0	308	69.1%
1	100	22.4%
2	14	3.1%
4	10	2.2%
3	8	1.8%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	308	69.1%
1	104	23.3%
2	12	2.7%
3	11	2.5%
4	6	1.3%
5	3	0.7%
8	2	0.4%

Value	Count	Frequency (%)
0	308	69.1%
1	100	22.4%
2	14	3.1%
3	8	1.8%
4	10	2.2%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	308	69.1%
1	100	22.4%
2	14	3.1%
3	8	1.8%
4	10	2.2%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	308	69.1%
1	104	23.3%
2	12	2.7%
3	11	2.5%
4	6	1.3%
5	3	0.7%
8	2	0.4%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.38116592	0.36995516

	Dataset A	Dataset B
Minimum	0	0
Maximum	6	6
Zeros	340	343
Zeros (%)	76.2%	76.9%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	0
95-th percentile	2	2
Maximum	6	6
Range	6	6
Interquartile range (IQR)	0	0

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.82815864	0.79599757
Coefficient of variation (CV)	2.1726985	2.1516056
Kurtosis	11.842398	10.474245
Mean	0.38116592	0.36995516
Median Absolute Deviation (MAD)	0	0
Skewness	3.0057124	2.8083941
Sum	170	165
Variance	0.68584673	0.63361213
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	340	76.2%
1	61	13.7%
2	37	8.3%
5	3	0.7%
4	2	0.4%
3	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	343	76.9%
1	56	12.6%
2	40	9.0%
4	3	0.7%
3	2	0.4%
5	1	0.2%
6	1	0.2%

Value	Count	Frequency (%)
0	340	76.2%
1	61	13.7%
2	37	8.3%
3	2	0.4%
4	2	0.4%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0	343	76.9%
1	56	12.6%
2	40	9.0%
3	2	0.4%
4	3	0.7%
5	1	0.2%
6	1	0.2%

Value	Count	Frequency (%)
0	343	76.9%
1	56	12.6%
2	40	9.0%
3	2	0.4%
4	3	0.7%
5	1	0.2%
6	1	0.2%

Value	Count	Frequency (%)
0	340	76.2%
1	61	13.7%
2	37	8.3%
3	2	0.4%
4	2	0.4%
5	3	0.7%
6	1	0.2%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	380	382
Distinct (%)	85.2%	85.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.7668161	6.7713004
Min length	3	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	3018	3020
Distinct characters	35	32
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	328	336 ?
Unique (%)	73.5%	75.3%

Sample

	Dataset A	Dataset B
1st row	250652	P/PP 3381
2nd row	P/PP 3381	347742
3rd row	347088	1601
4th row	S.O.P. 1166	374910
5th row	367655	220845

Value	Count	Frequency (%)
pc	27	4.8%
c.a	12	2.1%
a/5	7	1.2%
ca	6	1.1%
soton/oq	6	1.1%
ston/o	6	1.1%
2	6	1.1%
w./c	6	1.1%
c	5	0.9%
soton/o.q	5	0.9%
Other values (401)	481	84.8%

Value	Count	Frequency (%)
pc	32	5.7%
c.a	11	2.0%
ca	7	1.2%
2	6	1.1%
sc/paris	6	1.1%
a/5	6	1.1%
ston/o	6	1.1%
ston/o2	4	0.7%
soton/oq	4	0.7%
19950	4	0.7%
Other values (402)	477	84.7%

Most occurring characters

Value	Count	Frequency (%)
3	370	12.3%
1	355	11.8%
2	302	10.0%
7	242	8.0%
4	239	7.9%
6	201	6.7%
5	197	6.5%
0	185	6.1%
9	154	5.1%
8	149	4.9%
Other values (25)	624	20.7%

Value	Count	Frequency (%)
3	368	12.2%
1	349	11.6%
2	311	10.3%
7	239	7.9%
4	235	7.8%
6	208	6.9%
0	207	6.9%
5	194	6.4%
9	171	5.7%
8	133	4.4%
Other values (22)	605	20.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3018	100.0%

Value	Count	Frequency (%)
(unknown)	3020	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	370	12.3%
1	355	11.8%
2	302	10.0%
7	242	8.0%
4	239	7.9%
6	201	6.7%
5	197	6.5%
0	185	6.1%
9	154	5.1%
8	149	4.9%
Other values (25)	624	20.7%

Value	Count	Frequency (%)
3	368	12.2%
1	349	11.6%
2	311	10.3%
7	239	7.9%
4	235	7.8%
6	208	6.9%
0	207	6.9%
5	194	6.4%
9	171	5.7%
8	133	4.4%
Other values (22)	605	20.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3018	100.0%

Value	Count	Frequency (%)
(unknown)	3020	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	370	12.3%
1	355	11.8%
2	302	10.0%
7	242	8.0%
4	239	7.9%
6	201	6.7%
5	197	6.5%
0	185	6.1%
9	154	5.1%
8	149	4.9%
Other values (25)	624	20.7%

Value	Count	Frequency (%)
3	368	12.2%
1	349	11.6%
2	311	10.3%
7	239	7.9%
4	235	7.8%
6	208	6.9%
0	207	6.9%
5	194	6.4%
9	171	5.7%
8	133	4.4%
Other values (22)	605	20.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3018	100.0%

Value	Count	Frequency (%)
(unknown)	3020	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	370	12.3%
1	355	11.8%
2	302	10.0%
7	242	8.0%
4	239	7.9%
6	201	6.7%
5	197	6.5%
0	185	6.1%
9	154	5.1%
8	149	4.9%
Other values (25)	624	20.7%

Value	Count	Frequency (%)
3	368	12.2%
1	349	11.6%
2	311	10.3%
7	239	7.9%
4	235	7.8%
6	208	6.9%
0	207	6.9%
5	194	6.4%
9	171	5.7%
8	133	4.4%
Other values (22)	605	20.0%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	189	178
Distinct (%)	42.4%	39.9%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	31.077017	33.698981

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	6	11
Zeros (%)	1.3%	2.5%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.225	7.05105
Q1	7.925	7.8958
median	13.8604	14.4542
Q3	30	31.359375
95-th percentile	110.8833	130.2375
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	22.075	23.463575

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	46.188385	51.455387
Coefficient of variation (CV)	1.4862554	1.5269122
Kurtosis	32.29171	22.770471
Mean	31.077017	33.698981
Median Absolute Deviation (MAD)	6.6104	7.225
Skewness	4.5812418	4.0133961
Sum	13860.35	15029.746
Variance	2133.3669	2647.6568
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
8.05	25	5.6%
7.75	20	4.5%
7.8958	20	4.5%
13	18	4.0%
26	15	3.4%
10.5	14	3.1%
7.25	10	2.2%
7.925	9	2.0%
7.775	8	1.8%
7.225	8	1.8%
Other values (179)	299	67.0%

Value	Count	Frequency (%)
8.05	25	5.6%
13	23	5.2%
7.75	19	4.3%
7.8958	17	3.8%
26	14	3.1%
0	11	2.5%
10.5	11	2.5%
7.925	10	2.2%
26.55	10	2.2%
7.2292	9	2.0%
Other values (168)	297	66.6%

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
5	1	0.2%
6.2375	1	0.2%
6.45	1	0.2%
6.4958	2	0.4%
6.75	2	0.4%
6.95	1	0.2%
7.05	4	0.9%
7.0542	1	0.2%

Value	Count	Frequency (%)
0	11	2.5%
4.0125	1	0.2%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
6.975	1	0.2%

Value	Count	Frequency (%)
0	11	2.5%
4.0125	1	0.2%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
6.975	1	0.2%

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
5	1	0.2%
6.2375	1	0.2%
6.45	1	0.2%
6.4958	2	0.4%
6.75	2	0.4%
6.95	1	0.2%
7.05	4	0.9%
7.0542	1	0.2%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	83	85
Distinct (%)	86.5%	81.7%
Missing	350	342
Missing (%)	78.5%	76.7%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	11	15
Median length	3	3
Mean length	3.6145833	3.6634615
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	347	381
Distinct characters	18	18
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	71	69 ?
Unique (%)	74.0%	66.3%

Sample

	Dataset A	Dataset B
1st row	C54	C93
2nd row	C2	F E69
3rd row	D36	E17
4th row	E67	D35
5th row	F G73	B101

Value	Count	Frequency (%)
c23	3	2.7%
c25	3	2.7%
c27	3	2.7%
c93	2	1.8%
d35	2	1.8%
b28	2	1.8%
b22	2	1.8%
b96	2	1.8%
b98	2	1.8%
d26	2	1.8%
Other values (82)	89	79.5%

Value	Count	Frequency (%)
c23	4	3.3%
c25	4	3.3%
c27	4	3.3%
b96	3	2.4%
b98	3	2.4%
b58	2	1.6%
b60	2	1.6%
b20	2	1.6%
f4	2	1.6%
c124	2	1.6%
Other values (84)	95	77.2%

Most occurring characters

Value	Count	Frequency (%)
2	43	12.4%
C	35	10.1%
3	29	8.4%
B	28	8.1%
1	25	7.2%
6	22	6.3%
5	21	6.1%
8	20	5.8%
7	17	4.9%
	16	4.6%
Other values (8)	91	26.2%

Value	Count	Frequency (%)
C	40	10.5%
2	38	10.0%
B	35	9.2%
3	33	8.7%
1	27	7.1%
6	26	6.8%
5	24	6.3%
4	22	5.8%
9	19	5.0%
	19	5.0%
Other values (8)	98	25.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	347	100.0%

Value	Count	Frequency (%)
(unknown)	381	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	43	12.4%
C	35	10.1%
3	29	8.4%
B	28	8.1%
1	25	7.2%
6	22	6.3%
5	21	6.1%
8	20	5.8%
7	17	4.9%
	16	4.6%
Other values (8)	91	26.2%

Value	Count	Frequency (%)
C	40	10.5%
2	38	10.0%
B	35	9.2%
3	33	8.7%
1	27	7.1%
6	26	6.8%
5	24	6.3%
4	22	5.8%
9	19	5.0%
	19	5.0%
Other values (8)	98	25.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	347	100.0%

Value	Count	Frequency (%)
(unknown)	381	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	43	12.4%
C	35	10.1%
3	29	8.4%
B	28	8.1%
1	25	7.2%
6	22	6.3%
5	21	6.1%
8	20	5.8%
7	17	4.9%
	16	4.6%
Other values (8)	91	26.2%

Value	Count	Frequency (%)
C	40	10.5%
2	38	10.0%
B	35	9.2%
3	33	8.7%
1	27	7.1%
6	26	6.8%
5	24	6.3%
4	22	5.8%
9	19	5.0%
	19	5.0%
Other values (8)	98	25.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	347	100.0%

Value	Count	Frequency (%)
(unknown)	381	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	43	12.4%
C	35	10.1%
3	29	8.4%
B	28	8.1%
1	25	7.2%
6	22	6.3%
5	21	6.1%
8	20	5.8%
7	17	4.9%
	16	4.6%
Other values (8)	91	26.2%

Value	Count	Frequency (%)
C	40	10.5%
2	38	10.0%
B	35	9.2%
3	33	8.7%
1	27	7.1%
6	26	6.8%
5	24	6.3%
4	22	5.8%
9	19	5.0%
	19	5.0%
Other values (8)	98	25.7%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	2	1
Missing (%)	0.4%	0.2%
Memory size	7.0 KiB	7.0 KiB

S	322
C	82
Q	40

S	320
C	88
Q	37

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	444	445
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	C
2nd row	C	S
3rd row	S	S
4th row	S	S
5th row	Q	S

Common Values

Value	Count	Frequency (%)
S	322	72.2%
C	82	18.4%
Q	40	9.0%
(Missing)	2	0.4%

Value	Count	Frequency (%)
S	320	71.7%
C	88	19.7%
Q	37	8.3%
(Missing)	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	322	72.5%
c	82	18.5%
q	40	9.0%

Value	Count	Frequency (%)
s	320	71.9%
c	88	19.8%
q	37	8.3%

Most occurring characters

Value	Count	Frequency (%)
S	322	72.5%
C	82	18.5%
Q	40	9.0%

Value	Count	Frequency (%)
S	320	71.9%
C	88	19.8%
Q	37	8.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	444	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
S	322	72.5%
C	82	18.5%
Q	40	9.0%

Value	Count	Frequency (%)
S	320	71.9%
C	88	19.8%
Q	37	8.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	444	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
S	322	72.5%
C	82	18.5%
Q	40	9.0%

Value	Count	Frequency (%)
S	320	71.9%
C	88	19.8%
Q	37	8.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	444	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
S	322	72.5%
C	82	18.5%
Q	40	9.0%

Value	Count	Frequency (%)
S	320	71.9%
C	88	19.8%
Q	37	8.3%

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.000	0.119	-0.213	0.033	0.209	0.181	-0.169	0.169
Embarked	0.000	1.000	0.144	0.080	0.000	0.245	0.166	0.079	0.167
Fare	0.119	0.144	1.000	0.439	-0.068	0.469	0.169	0.432	0.250
Parch	-0.213	0.080	0.439	1.000	-0.028	0.000	0.249	0.486	0.159
PassengerId	0.033	0.000	-0.068	-0.028	1.000	0.000	0.000	-0.075	0.000
Pclass	0.209	0.245	0.469	0.000	0.000	1.000	0.084	0.123	0.307
Sex	0.181	0.166	0.169	0.249	0.000	0.084	1.000	0.218	0.527
SibSp	-0.169	0.079	0.432	0.486	-0.075	0.123	0.218	1.000	0.191
Survived	0.169	0.167	0.250	0.159	0.000	0.307	0.527	0.191	1.000

Dataset B

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.000	0.093	-0.216	-0.025	0.259	0.051	-0.231	0.100
Embarked	0.000	1.000	0.214	0.000	0.000	0.236	0.102	0.107	0.187
Fare	0.093	0.214	1.000	0.426	-0.006	0.454	0.231	0.439	0.347
Parch	-0.216	0.000	0.426	1.000	0.015	0.000	0.272	0.487	0.158
PassengerId	-0.025	0.000	-0.006	0.015	1.000	0.033	0.154	-0.074	0.150
Pclass	0.259	0.236	0.454	0.000	0.033	1.000	0.104	0.140	0.351
Sex	0.051	0.102	0.231	0.272	0.154	0.104	1.000	0.172	0.535
SibSp	-0.231	0.107	0.439	0.487	-0.074	0.140	0.172	1.000	0.175
Survived	0.100	0.187	0.347	0.158	0.150	0.351	0.535	0.175	1.000

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset A

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset B

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
417	418	1	2	Silven, Miss. Lyyli Karoliina	female	18.0	0	2	250652	13.0000	NaN	S
874	875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
819	820	0	3	Skoog, Master. Karl Thorsten	male	10.0	3	2	347088	27.9000	NaN	S
150	151	0	2	Bateman, Rev. Robert James	male	51.0	0	0	S.O.P. 1166	12.5250	NaN	S
388	389	0	3	Sadlier, Mr. Matthew	male	NaN	0	0	367655	7.7292	NaN	Q
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
622	623	1	3	Nakid, Mr. Sahid	male	20.0	1	1	2653	15.7417	NaN	C
310	311	1	1	Hays, Miss. Margaret Bechstein	female	24.0	0	0	11767	83.1583	C54	C
202	203	0	3	Johanson, Mr. Jakob Alfred	male	34.0	0	0	3101264	6.4958	NaN	S
603	604	0	3	Torber, Mr. Ernst William	male	44.0	0	0	364511	8.0500	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
874	875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
692	693	1	3	Lam, Mr. Ali	male	NaN	0	0	1601	56.4958	NaN	S
95	96	0	3	Shorney, Mr. Charles Joseph	male	NaN	0	0	374910	8.0500	NaN	S
754	755	1	2	Herman, Mrs. Samuel (Jane Laver)	female	48.0	1	2	220845	65.0000	NaN	S
39	40	1	3	Nicola-Yarred, Miss. Jamila	female	14.0	1	0	2651	11.2417	NaN	C
26	27	0	3	Emir, Mr. Farred Chehab	male	NaN	0	0	2631	7.2250	NaN	C
224	225	1	1	Hoyt, Mr. Frederick Maxfield	male	38.0	1	0	19943	90.0000	C93	S
422	423	0	3	Zimmerman, Mr. Leo	male	29.0	0	0	315082	7.8750	NaN	S
128	129	1	3	Peter, Miss. Anna	female	NaN	1	1	2668	22.3583	F E69	C

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
867	868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
136	137	1	1	Newsom, Miss. Helen Monypeny	female	19.0	0	2	11752	26.2833	D47	S
502	503	0	3	O'Sullivan, Miss. Bridget Mary	female	NaN	0	0	330909	7.6292	NaN	Q
308	309	0	2	Abelson, Mr. Samuel	male	30.0	1	0	P/PP 3381	24.0000	NaN	C
338	339	1	3	Dahl, Mr. Karl Edwart	male	45.0	0	0	7598	8.0500	NaN	S
572	573	1	1	Flynn, Mr. John Irwin ("Irving")	male	36.0	0	0	PC 17474	26.3875	E25	S
566	567	0	3	Stoytcheff, Mr. Ilia	male	19.0	0	0	349205	7.8958	NaN	S
328	329	1	3	Goldsmith, Mrs. Frank John (Emily Alice Brown)	female	31.0	1	1	363291	20.5250	NaN	S
449	450	1	1	Peuchen, Major. Arthur Godfrey	male	52.0	0	0	113786	30.5000	C104	S
736	737	0	3	Ford, Mrs. Edward (Margaret Ann Watson)	female	48.0	1	3	W./C. 6608	34.3750	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
442	443	0	3	Petterson, Mr. Johan Emil	male	25.0	1	0	347076	7.7750	NaN	S
228	229	0	2	Fahlstrom, Mr. Arne Jonas	male	18.0	0	0	236171	13.0000	NaN	S
443	444	1	2	Reynaldo, Ms. Encarnacion	female	28.0	0	0	230434	13.0000	NaN	S
198	199	1	3	Madigan, Miss. Margaret "Maggie"	female	NaN	0	0	370370	7.7500	NaN	Q
141	142	1	3	Nysten, Miss. Anna Sofia	female	22.0	0	0	347081	7.7500	NaN	S
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0	0	0	PC 17609	49.5042	NaN	C
547	548	1	2	Padro y Manent, Mr. Julian	male	NaN	0	0	SC/PARIS 2146	13.8625	NaN	C
561	562	0	3	Sivic, Mr. Husein	male	40.0	0	0	349251	7.8958	NaN	S
802	803	1	1	Carter, Master. William Thornton II	male	11.0	1	2	113760	120.0000	B96 B98	S
461	462	0	3	Morley, Mr. William	male	34.0	0	0	364506	8.0500	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)